"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class  snake_case__      :
 _snake_case       :					Union[str, Any]							  =						LEDConfig
 _snake_case       :					List[str]							  =						{}
 _snake_case       :					Optional[Any]							  =						"""gelu"""


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=False    ,		lowerCamelCase=99    ,		lowerCamelCase=32    ,		lowerCamelCase=2    ,		lowerCamelCase=4    ,		lowerCamelCase=37    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=20    ,		lowerCamelCase=2    ,		lowerCamelCase=1    ,		lowerCamelCase=0    ,		lowerCamelCase=4    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size

        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    eos_token_id
        __a       =    pad_token_id
        __a       =    bos_token_id
        __a       =    attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        __a       =    self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        __a       =    (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size						)
        __a       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)    ,		1						)
        __a       =    tf.concat([input_ids, eos_tensor]    ,		axis=1						)

        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

        __a       =    self.config_cls(
            vocab_size=self.vocab_size    ,		d_model=self.hidden_size    ,		encoder_layers=self.num_hidden_layers    ,		decoder_layers=self.num_hidden_layers    ,		encoder_attention_heads=self.num_attention_heads    ,		decoder_attention_heads=self.num_attention_heads    ,		encoder_ffn_dim=self.intermediate_size    ,		decoder_ffn_dim=self.intermediate_size    ,		dropout=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		eos_token_ids=[2]    ,		bos_token_id=self.bos_token_id    ,		pad_token_id=self.pad_token_id    ,		decoder_start_token_id=self.pad_token_id    ,		attention_window=self.attention_window    ,		**self.config_updates    ,		)
        __a       =    prepare_led_inputs_dict(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    tf.concat(
            [tf.zeros_like(lowerCamelCase						)[:, :-1], tf.ones_like(lowerCamelCase						)[:, -1:]]    ,		axis=-1    ,		)
        __a       =    global_attention_mask
        return config, inputs_dict






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    TFLEDModel(config=lowerCamelCase						).get_decoder()
        __a       =    inputs_dict["input_ids"]

        __a       =    input_ids[:1, :]
        __a       =    inputs_dict["attention_mask"][:1, :]
        __a       =    1

        # first forward pass
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		use_cache=lowerCamelCase						)

        __a ,   __a       =    outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        __a       =    ids_tensor((self.batch_size, 3)    ,		config.vocab_size						)
        __a       =    tf.cast(ids_tensor((self.batch_size, 3)    ,		2						)    ,		tf.inta						)

        # append to next input_ids and
        __a       =    tf.concat([input_ids, next_tokens]    ,		axis=-1						)
        __a       =    tf.concat([attention_mask, next_attn_mask]    ,		axis=-1						)

        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase						)[0]
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase						)[0]

        self.parent.assertEqual(next_tokens.shape[1]    ,		output_from_past.shape[1]						)

        # select random slice
        __a       =    int(ids_tensor((1,)    ,		output_from_past.shape[-1]						)						)
        __a       =    output_from_no_past[:, -3:, random_slice_idx]
        __a       =    output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowerCamelCase    ,		lowerCamelCase    ,		rtol=1E-3						)



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    ):
       if attention_mask is None:
              __a       =    tf.cast(tf.math.not_equal(a   ,    config.pad_token_id				)   ,    tf.inta				)
       if decoder_attention_mask is None:
              __a       =    tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape   ,    dtype=tf.inta				),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,    config.pad_token_id				)   ,    tf.inta				),
                  ]   ,    axis=-1   ,    )
       if head_mask is None:
              __a       =    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
       if decoder_head_mask is None:
              __a       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
           "decoder_input_ids": decoder_input_ids,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
       }






@require_tf
class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					Union[str, Any]							  =						(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 _snake_case       :					List[str]							  =						(TFLEDForConditionalGeneration,) if is_tf_available() else ()
 _snake_case       :					int							  =						(
     {
         """conversational""": TFLEDForConditionalGeneration,
         """feature-extraction""": TFLEDModel,
         """summarization""": TFLEDForConditionalGeneration,
         """text2text-generation""": TFLEDForConditionalGeneration,
         """translation""": TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _snake_case       :					List[Any]							  =						True
 _snake_case       :					Dict							  =						False
 _snake_case       :					Union[str, Any]							  =						False
 _snake_case       :					Tuple							  =						False


 def 						a__   ( self						):
        __a       =    TFLEDModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=lowerCamelCase						)


 def 						a__   ( self						):
        self.config_tester.run_common_tests()


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()
        __a       =    tf.zeros_like(inputs_dict["attention_mask"]						)
        __a       =    2
        __a       =    tf.where(
            tf.range(self.model_tester.seq_length						)[None, :] < num_global_attn_indices    ,		1    ,		inputs_dict["global_attention_mask"]    ,		)

        __a       =    True
        __a       =    self.model_tester.seq_length
        __a       =    self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(lowerCamelCase						):
               __a       =    outputs.decoder_attentions
               self.assertEqual(len(lowerCamelCase						)    ,		self.model_tester.num_hidden_layers						)
               self.assertListEqual(
                   list(decoder_attentions[0].shape[-3:]						)    ,		[self.model_tester.num_attention_heads, seq_length, seq_length]    ,		)

        def check_encoder_attentions_output(lowerCamelCase						):
               __a       =    [t.numpy() for t in outputs.encoder_attentions]
               __a       =    [t.numpy() for t in outputs.encoder_global_attentions]
               self.assertEqual(len(lowerCamelCase						)    ,		self.model_tester.num_hidden_layers						)
               self.assertEqual(len(lowerCamelCase						)    ,		self.model_tester.num_hidden_layers						)
               self.assertListEqual(
                   list(attentions[0].shape[-3:]						)    ,		[self.model_tester.num_attention_heads, seq_length, seq_length]    ,		)
               self.assertListEqual(
                   list(global_attentions[0].shape[-3:]						)    ,		[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    ,		)

        for model_class in self.all_model_classes:
               __a       =    True
               __a       =    False
               __a       =    False
               __a       =    model_class(lowerCamelCase						)
               __a       =    model(self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)						)
               __a       =    len(lowerCamelCase						)
               self.assertEqual(config.output_hidden_states    ,		lowerCamelCase						)
               check_encoder_attentions_output(lowerCamelCase						)

               if self.is_encoder_decoder:
                      __a       =    model_class(lowerCamelCase						)
                      __a       =    model(self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)						)
                      self.assertEqual(config.output_hidden_states    ,		lowerCamelCase						)
                      check_decoder_attentions_output(lowerCamelCase						)

               # Check that output attentions can also be changed via the config
               del inputs_dict["output_attentions"]
               __a       =    True
               __a       =    model_class(lowerCamelCase						)
               __a       =    model(self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)						)
               self.assertEqual(config.output_hidden_states    ,		lowerCamelCase						)
               check_encoder_attentions_output(lowerCamelCase						)

               # Check attention is always last and order is fine
               __a       =    True
               __a       =    True
               __a       =    model_class(lowerCamelCase						)
               __a       =    model(self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)						)

               self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    ,		len(lowerCamelCase						)						)
               self.assertEqual(model.config.output_hidden_states    ,		lowerCamelCase						)
               check_encoder_attentions_output(lowerCamelCase						)


 @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."						)
 def 						a__   ( self						):
        pass






 def 						a__   ( self						):
        # TODO: Head-masking not yet implement
        pass



def 	_lowerCamelCase(	a				):
       return tf.constant(a   ,    dtype=tf.intaa				)


SCREAMING_SNAKE_CASE__:Any									=			1e-4






@slow
@require_tf
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"						).led

        # change to intended input here
        __a       =    _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
        __a       =    _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
        __a       =    prepare_led_inputs_dict(model.config    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    model(**lowerCamelCase						)[0]
        __a       =    (1, 1024, 768)
        self.assertEqual(output.shape    ,		lowerCamelCase						)
        # change to expected output here
        __a       =    tf.convert_to_tensor(
            [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]    ,		)
        tf.debugging.assert_near(output[:, :3, :3]    ,		lowerCamelCase    ,		atol=1E-3						)






 def 						a__   ( self						):
        __a       =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"						)

        # change to intended input here
        __a       =    _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
        __a       =    _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
        __a       =    prepare_led_inputs_dict(model.config    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    model(**lowerCamelCase						)[0]
        __a       =    (1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape    ,		lowerCamelCase						)
        # change to expected output here
        __a       =    tf.convert_to_tensor(
            [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]    ,		)
        tf.debugging.assert_near(output[:, :3, :3]    ,		lowerCamelCase    ,		atol=1E-3    ,		rtol=1E-3						)

"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
